no code implementations • NeurIPS 2012 • Jiarong Jiang, Adam Teichert, Jason Eisner, Hal Daume
Users want natural language processing (NLP) systems to be both fast and accurate, but quality often comes at the cost of speed.
no code implementations • NeurIPS 2012 • He He, Jason Eisner, Hal Daume
However, it is important to note that these guarantees depend on how well the policy we found can imitate the oracle on the training data.
no code implementations • NeurIPS 2012 • Piyush Rai, Abhishek Kumar, Hal Daume
In this paper, we present a multiple-output regression model that leverages the covariance structure of the functions (i. e., how the multiple functions are related with each other) as well as the conditional covariance structure of the outputs.
no code implementations • NeurIPS 2011 • Abhishek Kumar, Piyush Rai, Hal Daume
In many clustering problems, we have access to multiple views of the data each of which could be individually used for clustering.
no code implementations • NeurIPS 2011 • Jiarong Jiang, Piyush Rai, Hal Daume
We consider a general inference setting for discrete probabilistic graphical models where we seek maximum a posteriori (MAP) estimates for a subset of the random variables (max nodes), marginalizing over the rest (sum nodes).
no code implementations • NeurIPS 2010 • Abhishek Kumar, Avishek Saha, Hal Daume
This paper presents a co-regularization based approach to semi-supervised domain adaptation.
no code implementations • NeurIPS 2010 • Arvind Agarwal, Samuel Gerber, Hal Daume
We present a novel method for multitask learning (MTL) based on {\it manifold regularization}: assume that all task parameters lie on a manifold.
no code implementations • NeurIPS 2009 • Piyush Rai, Hal Daume
Canonical Correlation Analysis (CCA) is a useful technique for modeling dependencies between two (or more) sets of variables.